RapidMiner AI Hub replacing RapidMiner Server + RapidMiner Studio 9.7 Release
RapidMiner has just released AI Hub, which is more of an evolution of the RapidMiner Server product line, than just a re-branding.
Server was originally developed to scale and offload computationally intensive tasks to servers, on-premise or in the cloud, and for data science teams to store processes and collaborate. Now it’s evolved into so much more with Go, Notebooks and Grafana.
See below for all the new features. For pricing – RapidMiner AI Hub
For product updates and promotions,
please join our newsletter.
New dashboard provides insight into executed and failed jobs, disk usage of projects, configured schedules, web services, and more.
New project-based repository for AI collaboration and governance. Diverse teams can work together on use cases in a central location across automated, visual and code-based authoring styles.
Projects includes fine-grained version control based on git standards. AI development lifecycles demand projects to be version-controlled, iterative, collaborative and governed.
Git-based version control tracks all changes as “snapshots” allowing users to easily “roll back” which enables smoother collaboration and conflict resolution.
Enterprise-grade identity & access management: To support collaboration at scale, a new identity and access management framework is introduced, including Single Sign-On across the platform (Go, Studio and AI Hub, JupyterHub, Grafana and our platform admin tools)
RapidMiner Notebooks, the JupyterLab interface in the RapidMiner platform, is now fully integrated with the new projects framework. Allowing seamless and easy collaboration of coders with other users and full traceability of the code-based work within the AI lifecycle.
Download AI Hub app from Google Play Store. Check and manage your system from anywhere. Designed for admins, the new app grants access to check jobs, schedules and other activities and react as needed.
RapidMiner Studio 9.7 new features:
RapidMiner Studio, AI Hub and JupyterHub, now support the concept of projects, enabling you to structure and isolate your work, allowing users to collaborate while maintaining a consistent state across the entire project.
Projects are versioned, providing features like:
Linear backup, you can always revert to a past state (nothing is lost, no matter what you do).
Each snapshot (project version) is fully consistent, so it’s easy to answer compliance questions like “which process trained this model”.
Traceability: snapshots log who did what, when and why (through user-written comments).
There’s a Git server used as the version control backend. This also enables storing files of arbitrary types like .py or .csv, making your projects whole.
Direct git access for everyone working via Git, e.g. Python coders. This allows seamless, two-way integration for projects between Studio users and coders.
Local repositories, created with RapidMiner Studio 9.7 or later can also take advantage of supporting all files on your computer (.py, .jpeg, .pdf, etc).
RapidMiner ExampleSets are now written to disk in a new file format – HDF5.
This format ensures stability and performance when storing large amounts of data. Also, Python and RapidMiner Studio can exchange data easier and faster than ever before.
Improvements to time series functionality
New operator to Integrate time series with different methods (cumulative sum / left and right riemann sum / trapezoidal rule)
Added the option to specify negative lags and a default lag for a set of attributes (selected by an attribute subset selector) to the Lag operator
Unfortunately due to parameter key incompatibilities, the old version of the ‘Lag’ operator had to be deprecated and new version with the same name, but different operator key is added.
Added options to use padding for Fast Fourier Transformation and calculate the frequency of the amplitude value.
Improvements to augmented machine learning
Auto Model reduces memory usage and run times and allows multiple Auto Model jobs to be submitted to the AI Hub at once.
Model Ops offers flexible model storage options for deployed models. Unused and ID columns are now kept in the results after scoring for enhanced audits.
Updated H2O library
Increases stability and performance for Gradient Boosted Trees, Logistic Regression, Deep Learning and Generalized Linear Model operators.
Gradient Boosted Trees now support monotonicity constraints. Deep Learning now exposes model weights on a separate output port. Model training can be fine-tuned using expert parameters. All parameters provided by H2O are supported.
Feel free to download a 30 day trial of RapidMiner Studio and experience enterprise AI software. After the trial, Studio is limited to 10k rows, 1 logical processor and no AutoML.